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• W2996750072 abstract "We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. We show how these two components contribute to ensuring accuracy parity and equalized false-positive and false-negative rates across groups without impacting demographic parity. Furthermore, we also demonstrate both in theory and on two real-world experiments that the proposed algorithm leads to a better utility-fairness trade-off on balanced datasets compared with existing algorithms on learning fair representations." @default.
• W2996750072 created "2019-12-26" @default.
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• W2996750072 date "2020-04-30" @default.
• W2996750072 modified "2023-09-24" @default.
• W2996750072 title "Conditional Learning of Fair Representations" @default.
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